研究者詳細 - 小田　昌宏

論文 - 小田　昌宏

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Depth-based branching level estimation for bronchoscopic navigation 査読有り

Wang Cheng, Hayashi Yuichiro, Oda Masahiro, Kitasaka Takayuki, Takabatake Hirotsugu, Mori Masaki, Honma Hirotoshi, Natori Hiroshi, Mori Kensaku

International Journal of Computer Assisted Radiology and Surgery 16 巻 ( 10 ) 頁： 1795 - 1804 2021年8月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：International Journal of Computer Assisted Radiology and Surgery

Purpose: Bronchoscopists rely on navigation systems during bronchoscopy to reduce the risk of getting lost in the complex bronchial tree-like structure and the homogeneous bronchus lumens. We propose a patient-specific branching level estimation method for bronchoscopic navigation because it is vital to identify the branches being examined in the bronchus tree during examination. Methods: We estimate the branching level by integrating the changes in the number of bronchial orifices and the camera motions among the frames. We extract the bronchial orifice regions from a depth image, which is generated using a cycle generative adversarial network (CycleGAN) from real bronchoscopic images. We calculate the number of orifice regions using the vertical and horizontal projection profiles of the depth images and obtain the camera-moving direction using the feature point-based camera motion estimation. The changes in the number of bronchial orifices are combined with the camera-moving direction to estimate the branching level. Results: We used three in vivo and one phantom case to train the CycleGAN model and four in vivo cases to validate the proposed method. We manually created the ground truth of the branching level. The experimental results showed that the proposed method can estimate the branching level with an average accuracy of 87.6%. The processing time per frame was about 61 ms. Conclusion: Experimental results show that it is feasible to estimate the branching level using the number of bronchial orifices and camera-motion estimation from real bronchoscopic images.

DOI： 10.1007/s11548-021-02460-8

Web of Science

Scopus

PubMed

深層学習を用いた医用画像処理と画像を超えた応用招待有り

小田昌宏

生体医工学 59 巻 ( 0 ) 頁： 256 - 256 2021年6月

担当区分：筆頭著者記述言語：日本語掲載種別：研究論文（研究会，シンポジウム資料等）出版者・発行元：公益社団法人日本生体医工学会

深層学習は医用画像処理において重要な手法の一つとなった．医用画像処理に含まれる画像分類，検出，セグメンテーションにおいては深層学習が多くの問題を解決し，高い成果を挙げている．画像モダリティに着目すると，眼底画像や放射線画像を対象とした深層学習の適用例が多く，実用化レベルに達したものもある．本発表では，手法や画像モダリティの面から深層学習を用いた医用画像処理を概観し，応用例を紹介する．また，深層学習の新たな応用についても紹介する．深層学習は画像に限らず様々なデータを説明変数と目的変数とすることが可能である．この性質を利用し，画像，テキスト，センサ計測データ等を用いる新たな手法を構築できる．応用例と共に深層学習の新たな可能性について考える．

DOI： 10.11239/jsmbe.Annual59.256

Artificial intelligence-assisted colonic endocytoscopy for cancer recognition: a multicenter study 査読有り

Yuichi Mori , Shin-ei Kudo , Masashi Misawa , Kinichi Hotta , Ohtsuka Kazuo , Shoichi Saito , Hiroaki Ikematsu , Yutaka Saito , Takahisa Matsuda , Takeda Kenichi , Toyoki Kudo , Tetsuo Nemoto , Hayato Itoh , Kensaku Mori

Endoscopy International Open 頁： E1004 - E1011 2021年6月

Unsupervised colonoscopic depth estimation by domain translations with a Lambertian-reflection keeping auxiliary task 査読有り

Itoh Hayato, Oda Masahiro, Mori Yuichi, Misawa Masashi, Kudo Shin-Ei, Imai Kenichiro, Ito Sayo, Hotta Kinichi, Takabatake Hirotsugu, Mori Masaki, Natori Hiroshi, Mori Kensaku

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERYInternational Journal of Computer Assisted Radiology and Surgery 16 巻 ( 6 ) 頁： 989 - 1001 2021年6月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：International Journal of Computer Assisted Radiology and Surgery

Purpose: A three-dimensional (3D) structure extraction technique viewed from a two-dimensional image is essential for the development of a computer-aided diagnosis (CAD) system for colonoscopy. However, a straightforward application of existing depth-estimation methods to colonoscopic images is impossible or inappropriate due to several limitations of colonoscopes. In particular, the absence of ground-truth depth for colonoscopic images hinders the application of supervised machine learning methods. To circumvent these difficulties, we developed an unsupervised and accurate depth-estimation method. Method: We propose a novel unsupervised depth-estimation method by introducing a Lambertian-reflection model as an auxiliary task to domain translation between real and virtual colonoscopic images. This auxiliary task contributes to accurate depth estimation by maintaining the Lambertian-reflection assumption. In our experiments, we qualitatively evaluate the proposed method by comparing it with state-of-the-art unsupervised methods. Furthermore, we present two quantitative evaluations of the proposed method using a measuring device, as well as a new 3D reconstruction technique and measured polyp sizes. Results: Our proposed method achieved accurate depth estimation with an average estimation error of less than 1 mm for regions close to the colonoscope in both of two types of quantitative evaluations. Qualitative evaluation showed that the introduced auxiliary task reduces the effects of specular reflections and colon wall textures on depth estimation and our proposed method achieved smooth depth estimation without noise, thus validating the proposed method. Conclusions: We developed an accurate depth-estimation method with a new type of unsupervised domain translation with the auxiliary task. This method is useful for analysis of colonoscopic images and for the development of a CAD system since it can extract accurate 3D information.

DOI： 10.1007/s11548-021-02398-x

Web of Science

Scopus

PubMed

Deep learning system for automatic detection of bladder tumors in cystoscopic images 査読有り

Mutaguchi J., Oda M., Ueda S., Kinoshita F., Naganuma H., Matsumoto T., Lee K., Monji K., Kashiwagi K., Takeuchi A., Shiota M., Inokuchi J., Mori K., Eto M.

EUROPEAN UROLOGY 79 巻頁： S1022 - S1023 2021年6月

COVID-19診断支援AI開発における名古屋大学の取り組み招待有り

小田昌宏, 鄭通, 林雄一郎, 森健策

Medical Imaging Technology 39 巻 ( 1 ) 頁： 13 - 19 2021年2月

Extremely imbalanced Subarachnoid Hemorrhage detection based on DenseNet-LSTM network with Class-Balanced Loss and Transfer Learning 査読有り

Lu Zhongyang, Oda Masahiro, Hayashi Yuichiro, Hu Tao, Itoh Hayato, Watadani Takeyuki, Abe Osamu, Hashimoto Masahiro, Jinzaki Masahiro, Mori Kensaku

MEDICAL IMAGING 2021: COMPUTER-AIDED DIAGNOSIS 11597 巻 2021年2月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Subarachnoid Hemorrhage (SAH) detection is a critical, severe problem that confused clinical residents for a long time. With the rise of deep learning technologies, SAH detection made a significant breakthrough in recent ten years. Whereas, the performances are significantly degraded on imbalanced data, makes deep learning models have always suffered criticism. In this study, we present a DenseNet-LSTM network with Class-Balanced Loss and the transfer learning strategy to solve the SAH detection problem on an extremely imbalanced dataset. Compared to the previous works, the proposed framework not merely effectively integrate greyscale features the and spatial information from the consecutive CT scans, but also employ Class-Balanced loss and transfer learning to alleviate the adverse effects and broaden feature diversity respectively on an extreme SAH cases scarcity dataset, mimicking the actual situation of emergency departments. Comprehensive experiments are conducted on a dataset, consisted of 2,519 cases without hemorrhage cases and only 33 cases with SAH. Experimental results demonstrate the F-measure score of SAH detection achieved a remarkable improvement, the backbone DenseNet121 gained around 33% promotion after transfer learning, and on this basis, importing the Class-Balanced Loss and the LSTM structure, the F-measure score further increased 6.1% and 2.7% sequentially.

DOI： 10.1117/12.2582088

Web of Science

Scopus

Automated Detection of Spinal Schwannomas Utilizing Deep Learning Based on Object Detection from MRI 査読有り

Sadayuki Ito, Kei Ando, Kazuyoshi Kobayashi, Hiroaki Nakashima Masahiro Oda, Masaaki Machino, Shunsuke Kanbara, Taro Inoue, Hidetoshi Yamaguchi, Hiroyuki Koshimizu, Kensaku Mori, Naoki Ishiguro, Shiro Imagama

Spine 46 巻 ( 2 ) 頁： 95 - 100 2021年1月

Current status and future perspective on artificial intelligence for lower endoscopy 査読有り

Misawa Masashi, Kudo Shin-ei, Mori Yuichi, Maeda Yasuharu, Ogawa Yushi, Ichimasa Katsuro, Kudo Toyoki, Wakamura Kunihiko, Hayashi Takemasa, Miyachi Hideyuki, Baba Toshiyuki, Ishida Fumio, Itoh Hayato, Oda Masahiro, Mori Kensaku

DIGESTIVE ENDOSCOPY 33 巻 ( 2 ) 頁： 273 - 284 2021年1月

医療支援AIの現状と情報横断型AIへの期待招待有り

小田昌宏

日本整形外科学会雑誌 95 巻 ( 1 ) 頁： 3 - 8 2021年1月

Current status and future perspective on artificial intelligence for lower endoscopy 査読有り

Misawa M., Kudo S.E., Mori Y., Maeda Y., Ogawa Y., Ichimasa K., Kudo T., Wakamura K., Hayashi T., Miyachi H., Baba T., Ishida F., Itoh H., Oda M., Mori K.

Gastroenterological Endoscopy 63 巻 ( 7 ) 頁： 1402 - 1416 2021年1月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Gastroenterological Endoscopy

The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesions. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one-fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for nonexperts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed）which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence-assisted computer-aided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep-learning, data-driven machine learning techniques have made the development of computer-aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer-aided diagnosis by colonoscopy. To date, computer-aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer-aided characterization systems may have the potential to improve diagnostic accuracy in real-time clinical practice. Furthermore, some artificial intelligence-assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer-aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real-time clinical decision-making. In this review, we have focused on computer-aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects.

DOI： 10.11280/gee.63.1402

Scopus

第1回JSCAS AI Challenge開催報告招待有り

森健策, 小田昌宏, 平尾彰浩, 澁澤喜人, 竹下修由, 伊藤雅昭

日本コンピュータ外科学会誌 23 巻 ( 2 ) 頁： 74 - 75 2021年

ディープラーニングによる物体検出：膀胱内視鏡における膀胱癌診断

牟田口淳, 小田昌宏, 小林聡, 猪口淳一, 森健策, 江藤正俊

生体医工学 59 巻 ( 0 ) 頁： 299 - 299 2021年

記述言語：日本語出版者・発行元：公益社団法人日本生体医工学会

【背景】膀胱癌は経尿道手術後に再発が多い腫瘍であり、膀胱鏡での腫瘍の見落としが原因とされている。従来の白色光(WLI)に加え、NBIは腫瘍検出の精度が改善する報告があるが、検者の経験に依存するため、再現性・客観性が乏しいことが課題である。近年、客観的な診断ツールとして人工知能(AI)が活用されているが、膀胱鏡においてAIによる腫瘍検出の研究はあまり行われていない。今回、我々はWLI/NBI膀胱鏡画像を用いて、AIによる腫瘍検出の精度を検証した。【方法】2019年12月から2020年11月まで、経尿道的膀胱腫瘍切除術(TURBT)の際に、WLI/NBIを用いて観察を行った症例の手術動画から膀胱鏡画像を作成し、腫瘍を含む画像を腫瘍画像、腫瘍を含まない画像を正常画像と定義した。腫瘍画像を学習データ、テストデータに分類し、AIによる感度、特異度、陽性的中率を評価した。AIでの物体検出はtiny-YOLOを用い、医師個人のコンピューター上で環境構築を行った。【結果】腫瘍画像(WLI:809枚、NBI:684枚)をそれぞれtiny-YOLOで学習を行い、腫瘍画像(WLI:280枚、NBI:87枚)と正常画像(WLI:104枚、NBI:104枚)で精度検証を行った。AIによる物体検出の感度/特異度/陽性的中率は、WLIで84.6%/76.9%/90.8%、NBIで78.1%/97.1%/95.8%であった。【結論】膀胱鏡画像において、AIにより比較的良好に腫瘍検出が可能であった。更なる精度改善、リアルタイム検出への課題について、文献的考察を加え報告する。

DOI： 10.11239/jsmbe.Annual59.299

深層学習による画像認識入門（2） GPU 環境構築と画像の領域分割招待有り

原武史, 小田昌宏

Medical Imaging Technology 39 巻 ( 3 ) 頁： 124 - 130 2021年

記述言語：日本語掲載種別：研究論文（学術雑誌）出版者・発行元：日本医用画像工学会

深層学習において本格的な実験や研究を行うためには，GPUを使った環境構築が必要となる．これは，CPUのみで計算した場合と比較して数十倍の計算速度が得られるためである．今回の講座では，GPU環境の構築方法を説明し，画像の領域分割を例にして，計算時間がどの程度変化するかを明らかにする．同時に，U-Netを用いた領域分割の方法，その正解データの構築についても言及する．

DOI： 10.11409/mit.39.124

特集／説明可能なAIの実現に向けて―序文― 招待有り

藤吉弘亘, 小田昌宏

Medical Imaging Technology 39 巻 ( 3 ) 頁： 97 - 98 2021年

Robust endocytoscopic image classification based on higher-order symmetric tensor analysis and multi-scale topological statistics 査読有り

Hayato Itoh, Yukitaka Nimura, Yuichi Mori, Masashi Misawa, Shin-Ei Kudo, Kinichi Hotta, Kazuo Ohtsuka, Shoichi Saito, Yutaka Saito, Hiroaki Ikematsu, Yuichiro Hayashi, Masahiro Oda, Kensaku Mori

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 15 巻 ( 12 ) 頁： 2049 - 2059 2020年12月

Synthetic laparoscopic video generation for machine learning-based surgical instrument segmentation from real laparoscopic video and virtual surgical instruments 査読有り

Ozawa Takuya, Hayashi Yuichiro, Oda Hirohisa, Oda Masahiro, Kitasaka Takayuki, Takeshita Nobuyoshi, Ito Masaaki, Mori Kensaku

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2020年11月

大腸鏡画像にYOLO-v3を用いた、穿孔の検出と局在化の比率損失設計に関する予備的研究(Preliminary Study of Loss-Functions Design for Detection and Localization of Perforations with YOLO-v3 in Colonoscopic Images)

蒋凱, 伊東隼人, 小田昌宏, 奥村大志, 森悠一, 三澤将史, 林武雅, 工藤進英, 森健策

日本コンピュータ外科学会誌 22 巻 ( 4 ) 頁： 348 - 349 2020年11月

SUN database 大腸ポリープ自動検出器の精度評価に向けた試験用画像

伊東隼人, 三澤将史, 森悠一, 小田昌宏, 工藤進英, 森健策

日本コンピュータ外科学会誌 22 巻 ( 4 ) 頁： 346 - 347 2020年11月

【多元計算解剖学の診断・治療・医工学への展開】人工知能に基づく医療機器EndoBRAINの臨床導入薬事承認取得・保険算定への挑戦

森悠一, 工藤進英, 三澤将史, 伊東隼人, 小田昌宏, 森健策

MEDICAL IMAGING TECHNOLOGY 38 巻 ( 5 ) 頁： 213 - 216 2020年11月

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